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BOREAL: Représentation de Connaissances et Langages à Base de Règles pour Raisonner sur les Données

Federico ULLIANA
Head
LIRMM_JF Baget
Jean-François BAGET
deputy

BOREAL team

Knowledge Representation and Rule-based Languages for Reasoning on Data

BOREAL : Knowledge Representation and Rule-based Languages for Reasoning on Data

Current information systems are grounded on the exploitation of data coming from an increasing number of heterogeneous sources. Coping with the variety of data requires paradigms for effectively accessing and querying information that adapt to the different types of sources, as well as declarative high-level languages to drive the data processing and data quality tasks. The BOREAL team focuses on the study of foundational and applied issues of reasoning, in a context of data variety. The team builds upon its expertise in knowledge representation and automated reasoning to devise novel techniques for heterogeneous and federated data management which leverage in particular on expressive rule languages.

The team focuses on a set of issues related to knowledge-based data management which include:

  • Foundations of rule languages (Existential Rules, Description Logics).
  • Algorithms and optimizations for reasoning on data.
  • Architectures and rule languages for heterogeneous data integration.
  • Inconsistency handling in query answering.
  • Quality of knowledge-based data integration systems.
  • Explanation of reasoning

Staff
Michel Leclere, Maître de conférences, UM
Marie-Laure Mugnier, Professeur des universités, UM
David Carral Martinez, Chargé de recherche, INRIA
Nofar Carmeli, , INRIA
Madalina Croitoru, Professeur des universités, UM
Federico Ulliana, Chargé de recherche, INRIA
Annie Aliaga, Assistant ingénieur, INRIA
Jean-François Baget, Chargé de recherche, INRIA


Associates & Students
Olivier Rodriguez, UM
Guillaume Perution Kihli, INRIA
Mohamed Aziz Sfar Gandoura, EDF


Regular Co-workers
Michel Chein, Invité longue durée Eméritat, UM
Florent Tornil, CDD Ingénieur-Technicien, INRIA